Sequential Projection-Based Metacognitive Learning in a Radial Basis Function Network for Classification Problems

Abstract

In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.

An incremental meta-cognitive-based scaffolding fuzzy neural network

“…The consolidation of the meta-cognitive aspect in machine learning was initiated by Suresh et al [7][8][9][10][11] based on a prominent meta-memory model proposed by Nelson and Naren [6]. The works in [7][8][9][10][11] identify that the meta-cognitive component, namely what-to-learn, how-to-learn and when-to-learn, can respectively be modelled with sample deletion strategy, sample learning strategy and sample reserved strategy.…”

“…The works in [7][8][9][10][11] identify that the meta-cognitive component, namely what-to-learn, how-to-learn and when-to-learn, can respectively be modelled with sample deletion strategy, sample learning strategy and sample reserved strategy. Nevertheless, their pioneering works still discount the construct of Scaffolding theory [12,22], rendering a plug-and-play classifier.…”

“…The Pþ method also deciphers the rule recall process, manifesting the problematizing component of Scaffolding theory to cope with the recurring concept drift; the Fuzzily Weighted Generalized Recursive Least Square (FWGRLS) method is integrated to adjust the rule consequent of the fuzzy rule and in turn delineates the passive supervision of the Scaffolding theory. gClass operates as its counterparts in [7][8][9][10][11], where the sample reserved strategy is employed in the when-to-learn process. Nonetheless, several new learning modules are proposed in this paper:…”

“…This modification makes the online active learning method more robust against outliers and more accurate in deciding the sample ignorance. Note that this mechanism can be also perceived as an enhanced version of the original what-to-learn module in [7][8][9][10][11]. In [7][8][9][10][11], the whatto-learn module is limited to ruling out redundant samples for model updates, and still assumes that data are fully labelled.…”

A Metacognitive Fully Complex Valued Functional Link Network for solving real valued classification problems

“…In order to overcome the drawback existing with the real valued meta-cognitive interval type 2 neuro fuzzy inference system, a complex valued meta-cognitive interval type 2 neuro fuzzy inference system has been proposed in [45]. One can refer to [22,[46][47][48] for additional details regarding metacognition concepts. In order to train the network sequentially, a Meta-cognitive fully complex valued functional link network with polynomial function in the input layer is presented.…”